Improving Coastal and Port Management in Smart Cities with UAVs and Deep Learning

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:52Z
dc.date.available2025-03-17T12:22:52Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description1st Mediterranean Smart Cities Conference, MSCC 2024 -- 2 May 2024 through 4 May 2024 -- Martil - Tetuan -- 203162
dc.description.abstractEfficient coast and harbor management is integral to the vitality, sustainability, and resilience of smart cities. With bustling harbors serving as vital hubs of commerce, trade, and tourism, optimizing port operations is paramount for economic growth and prosperity. Smart technologies play a pivotal role in this optimization, leveraging advanced sensor networks, real-time monitoring systems, and predictive analytics to enhance safety, mitigate environmental risks, and improve overall efficiency. Additionally, smart coastal management strategies focus on preserving ecosystems, mitigating climate change impacts, and safeguarding against natural disasters. Aerial imagery, facilitated by Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras and sensors, provides comprehensive insights into coastal dynamics, harbor operations, and environmental conditions. These images enable efficient monitoring of coastal areas, ports, and harbors, capturing crucial information for informed decision-making in coastal management and port operations. Object detection, particularly in ship detection, stands as a transformative technology for enhancing coastal and harbor management within smart cities. Leveraging advanced algorithms and high-resolution aerial imagery, ship detection systems offer real-time monitoring crucial for optimizing maritime operations and ensuring port security. Object detection algorithms, particularly Faster R-CNN, have shown promise in accurately detecting ships in aerial imagery, offering valuable insights for harbor planning and infrastructure development. This study focuses on utilizing the Faster R-CNN detection algorithm for ship detection in coastal and harbor environments, highlighting its potential to bolster security applications and contribute to the resilience of smart city infrastructure. Through rigorous evaluation and optimization, this research aims to enhance the effectiveness of ship detection systems in safeguarding coastal and harbor environments within smart cities. ©2024 IEEE.
dc.identifier.doi10.1109/MSCC62288.2024.10697069
dc.identifier.isbn979-835037400-1
dc.identifier.scopus2-s2.0-85207079631
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/MSCC62288.2024.10697069
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1416
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings of 2024 1st Edition of the Mediterranean Smart Cities Conference, MSCC 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectcoastal management
dc.subjectFaster R-CNN
dc.subjectship detection
dc.subjectsmart cities
dc.subjectunmanned aerial vehicle
dc.titleImproving Coastal and Port Management in Smart Cities with UAVs and Deep Learning
dc.typeConference Object

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